The domain shifts in LiDAR-based 3D objectors are obvious as the LiDARs have different scan patterns. Evaluation of datasets captured in different locations, conditions, or sensors than that of the training (source) data results in a drop in model performance due to the gap in distribution with the test (or target) data. To further investigate the generalization ability of Voxel-MAE in representation learning, we conduct experiments in two scenarios of unsupervised domain adaptative (UDA) 3D object detection: different data collection locations and time (i.e., Waymo \(\rightarrow \)  KITTI) and different LiDAR ring numbers (i.e., nuScenes \(\rightarrow \)  KITTI). We compare our method with (1) Oracle, where the model training and evaluation are both on the target domain; (2) Soure-only, where the pre-trained model is directly used on the target domain inferring; (3) SN [1]}, a weakly-supervised domain adaptation method with the statistical information of target domain object size; (4) ST3D [2]}, the SOTA UDA 3D object detection method with self-training strategy and (5) ST3D(w/SN) [2]}, where the weakly-supervised DA setting utilizes the target object size distribution as prior. We follow the setting in the recently published UDA method ST3D [2]} and use the pre-trained model by our Voxel-MAE on both the source and target data to train ST3D. From Table REF , we can see that with our Voxel-MAE, the performance of UDA is improved, which demonstrates that our Voxle-MAE can learn more transferable representations.
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